Deep Reinforcement Learning (DRL) shows good performance for optimizing battery energy storage systems (BESS) coordinated operations with photovoltaic plants (PV), yet most studies rely on simulations. Bridging the gap to practical application requir...
Environmental monitoring and assessment
Jan 3, 2026
Artificial neural networks (ANNs) are widely applied in air quality modelling because they can capture nonlinear interactions among pollutants and support reliable air pollutant index (API) forecasting. This study aims to identify the pollutants that...
Urban expansion and Land Use Land Cover (LULC) change pose critical challenges for sustainable urban planning and risks to food security. This study analyzes multi-temporal Landsat imagery from 1990 to 2020 for five major cities, Islamabad, Karachi, ...
This study proposes an innovative hybrid forecasting model, VMD-CPSO-BiLSTM, which significantly enhances the prediction accuracy of shipping indices in China's maritime sector. The model employs a sophisticated three-phase methodology: (1) decomposi...
Long-term time series forecasting is critical for domains such as traffic and energy systems, yet contemporary models often fail to capture complex multiscale patterns and nonlinear dynamics, resulting in significant inaccuracies during periods of ab...
BACKGROUND: Although strategies for COVID-19 have shifted towards normalized measures globally, establishing predictive models based on Internet search data remains crucial for swiftly controlling and preventing future outbreaks. This study aims to u...
The study investigates the application of Artificial Intelligence (AI) driven neural network time series (NNTS) model for the forecasting prediction of dye removal using ultrasonic activated mixed biomass. Surface and functional characterization of u...
Urban air pollution poses a significant threat to public health and urban sustainability in megacities like Paris. We cast forecasting as a short-term, next-hour prediction task for PM2.5, NO, and CO, using hourly meteorology and recent pollutant his...
Sepsis-induced glucose fluctuations present major challenges in critical care, underscoring the importance of accurate glucose monitoring and forecasting to improve patient outcomes. This study introduces a suite of forecasting models trained using c...
To improve the intelligent and refined management level of power distribution systems in equipment operation and maintenance as well as emergency support, this work proposes an integrated "prediction-optimization" model that combines genetic algorith...
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